Explainability and Complementary Information for Camera-Based Quality Assurance Inspection Processes

2. The method of claim 1, where the overlay image contains color and transparency information derived from the generated quality assurance metrics for the object.

3. The method of claim 1, wherein a first of the image analysis inspection tools utilizes computer vision algorithms in generating quality assurance metrics and a second of the image analysis inspection tools utilizes machine learning in generating quality assurance metrics.

4. The method of claim 1, wherein at least a portion of the overlay images are empty.

5. The method of claim 1, wherein overlay images are only generated when the quality assurance metrics are above a defined threshold.

7. The method of claim 1, wherein providing the enhanced images comprises one or more of: visually displaying the enhanced images in an electronic visual display, transmitting the enhanced images to a remote computing system, loading the enhanced images into memory, or storing the enhanced images in physical persistence.

8. The method of claim 1, wherein the received data is derived from a video feed of a manufacturing production line for the objects.

9. The method of claim 1, wherein providing the enhanced image comprises: compressing the enhanced image to a video stream.

11. The method of claim 10, wherein the anomaly detector comprises one or more convolutional neural networks.

12. The method of claim 1, wherein one of the quality assurance inspection tools executes one or more dimensional modification algorithms to cause a dimension of the image to more closely reflect a reference image.

13. The method of claim 1, wherein one of the quality assurance inspection tools modifies a color space for the images.

14. The method of claim 1, wherein one of the quality assurance inspection tools causes an image to be sharpened or blurred.

15. The method of claim 1, wherein a first color in the overlay image corresponds to a first result and a second color in the overlay image corresponds to a second, different result.

16. The method of claim 15, wherein the first result is pass and the second result is fail.

18. The system of claim 17, wherein a first of the image analysis inspection tools utilizes computer vision algorithms in generating quality assurance metrics and a second of the image analysis inspection tools utilizes machine learning in generating quality assurance metrics.

20. The method of claim 19, wherein the convolutional neural network is trained to detect certain classes, and the overlay image visually and distinctly indicates the detected certain classes.